Rationale: In tandem mass spectrometry (MS/MS)-based proteomics, precise prediction of peptide spectra is key to improving peptide identification accuracy and the overall reliability of proteomic studies. However, existing theoretical MS/MS prediction methods are limited by their focus on predicting only b and y backbone fragment ions and their inability to effectively capture the complex long-range dependencies among distant residues within peptide sequences.
Methods: To address these issues, we propose DeepMultiIon, a novel prediction framework based on a mathematically grounded recursive attention mechanism, designed to enhance MS/MS spectral fitting and improve peptide detection sensitivity. By combining local chunking with recursive attention, the model enables deep modeling of long-range residue interactions and the corresponding fragment intensity dependencies. In addition to conventional b and y backbone fragment ions, DeepMultiIon further extends its predictive coverage to a ions and precursor ions, while also encompassing their neutral loss ions (-H2O, -NH3) and isotopic peaks, offering comprehensive coverage of all major spectral peak types.
Results: Performance is evaluated using Pearson correlation coefficient (PCC) and entropy similarity (ES), with comparisons against b/y-only models such as Prosit, pDeep, and AlphaPeptDeep. Experimental results show that DeepMultiIon achieves notable improvements, with average increases of 0.18 in PCC and 0.22 in ES.
Conclusions: These findings demonstrate that incorporating recursive attention and multi-ion prediction significantly improve both the peak coverage and accuracy of theoretical MS/MS spectrum prediction, thereby enhancing peptide-spectrum fitting and contributing to improved peptide detection sensitivity, making DeepMultiIon a powerful tool for advanced proteomics analysis.
Keywords: entropy similarity; fragment ions; peptide sequences; proteomics; recursive attention; theoretical tandem mass spectrometry prediction.
© 2026 John Wiley & Sons Ltd.